Generalized method of moments (GMM) is a generic method for estimating parameters in statistical models. Usually it is applied in the context of semiparametric models, where the parameter of interest is finite-dimensional, whereas the full shape of the distribution function of the data may not be known, and therefore the maximum likelihood estimation is not applicable.

The following matlab project contains the source code and matlab examples used for gmm.

The source code and files included in this project are listed in the project files section, please make sure whether the listed source code meet your needs there.

## Project Files:

Mfcc.png in matlab

Community detection use gaussian mixture model in matlab

Fast gmm and fisher vectors in matlab

Ziheng gmm in matlab

Em algorithm for gaussian mixture model with background noise in matlab

Gaussian mixture model in matlab

Useful matlab functions for speaker recognition using adapted gaussian mixture model

Gaussian mixture modeling gui (gmm demo) in matlab

Gmm based expectation maximization algorithm in matlab

Expectation maximization algorithm with gaussian mixture model in matlab

Gaussian mixture model (gmm) gaussian mixture regression (gmr) in matlab

3d visualization of gmm learning via the em algorithm in matlab

Speaker recognition system in matlab

Expectation maximization of gaussian mixture models via cuda in matlab

Wrapper of the jmef java library in matlab

Fast kernel density estimator (multivariate) in matlab

Gmmem based pixel labeling and segmentation in matlab

The lamb toolbox in matlab

Statistical learning toolbox in matlab

Toolkit on econometrics and economics teaching in matlab